Research Article
Online Missing Data Imputation Using Virtual Temporal Neighbor in Wireless Sensor Networks
Algorithm 2
VTN imputation algorithm.
| Input: input data matrix (), node , sensor , , | | | Output: input data matrix () | | | if there exists a new data in IDM then | | (27) | | | (28) | U | | (29) | V | | (30) | vtn | | (31) | Get the new data from IDM by v_index | | (32) | Call Calculation of VTN | | (33) | | | (34) | if then | | (35) | Add c_index into imputation_index | | (36) | end if | | (37) | if then | | (38) | for each element in do | | (39) | if then | | (40) | Add into imputation_index | | (41) | end if | | (42) | end for | | (43) | end if | | (44) | if then | | (45) | Sort imputation_index by increasing index | | (46) | for each element j in imputation_index do | | (47) | for each k in 1: j do | | (48) | if then | | (49) | Add into | | (50) | end if | | (51) | end for | | (52) | sort by increasing order of | | (53) | | | (54) | | | (55) | high | | (56) | if then | | (57) | | | (58) | else if high | | (59) | high | | (60) | end if | | (61) | | | (62) | | | (63) | for each element m in PAST_VTNM_CANDADATE do | | (64) | CHANGE_RATE_DIST | | (65) | | | (66) | end for | | (67) | sort by increasing order of CHANGE_RATE_DIST | | (68) | Remove imputing value from | | (69) | | | (70) | | | (71) | Construct the estimation equation using PAST_VTNM_CANDADATE and IDM_CANDIDATE to | | (72) | regress the coefficients | | (73) | Compute using and | | (74) | | | (75) | end for | | (76) | end if | | (77) | end if | | (78) | return IDM |
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